
cat /home/ysjm/liz/ner/BERT-NER-Pytorch-master/outputs_span/cnertianchi_output/bert/eval_results.txt
#base
#acc = 0.932993445010925
#f1 = 0.8480635551142005
#loss = 0.07684822044175235
#recall = 0.7773058252427184

#wwm 89.5062-20210707_11-43-32_446_ddm_addr_parsing_runid.txt
#acc = 0.9448454163524722
#f1 = 0.9150279067341297
#loss = 0.07391255128192645
#recall = 0.8870347896440129

#ernie
#acc = 0.9327086882453152
#f1 = 0.8466806583452999
#loss = 0.07734998999600287
#recall = 0.7751820388349514

#
#acc = 0.9331954246775371
#f1 = 0.8471225008284546
#loss = 0.07694310040172098
#recall = 0.7755865695792881


cat /home/ysjm/liz/ner/BERT-NER-Pytorch-master/outputs/cnertianchi_output/bert/eval_results.txt
#bert-base
#acc = 0.934799789251844
#f1 = 0.9349845201238389
#loss = 3.671778758606279
#recall = 0.9351693240216102

#wwm
#acc = 0.9353902953586498
#f1 = 0.9350820536479272
#loss = 3.6522152782922768
#recall = 0.934774015021742

#enie
#acc = 0.9362964916908467
#f1 = 0.9358644782809308
#loss = 3.327656672661563
#recall = 0.9354328633548558


cat /home/ysjm/liz/ner/BERT-NER-Pytorch-master/outputs/cnertianchi_output/bert/eval_results.txt

# softmaxbert:

#base
#acc = 0.9322011410375481
#f1 = 0.9289964299881
#loss = 0.22871985756570384
#recall = 0.9258136776913954

#wwm
#acc = 0.9351150113016886
#f1 = 0.9309066843150231
#loss = 0.23184357015859514
#recall = 0.9267360653577547

#ernie
#acc = 0.9318121163597545
#f1 = 0.9259431147649672
#loss = 0.2452562286828955
#recall = 0.9201475820266175

# span
sz outputs_span/cnertianchi_output/bert/test_predict.json

head outputs_span/cnertianchi_output/bert/test_predict.json


head outputs/cnertianchi_output/bert/test_predict.json


